Add to ORKGThe aim of this paper is to provide simple nonparametric methods to estimate finitemixture models from data with repeated measurements. Three measurements suffice for the mixture to be fully identified and so our approach can be used even with very short panel data. We provide distribution theory for estimators of the mixing proportions and the mixture distributions, and various functionals thereof. We also discuss inference on the number of components. These estimators are found to perform well in a series of Monte Carlo exercises. We apply our techniques to document heterogeneity in log annual earnings using PSID data spanning the period 1969-1998
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression ...
Mixtures of distributions are present in many econometric models, such as models with unobserved het...
The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models fr...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
The aim is to study the asymptotic behavior of estimators and tests for the components of identifiab...
Estimating the model evidence - or mariginal likelihood of the data - is a notoriously difficult tas...
In many applications, observations from some distribution of interest are contaminated with errors...
Finite mixtures of probability distributions may be successfully used in the modeling of probability...
A recorded signal frequently results from the mixture of many signals from several classifiable sour...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression ...
Mixtures of distributions are present in many econometric models, such as models with unobserved het...
The aim of this paper is to provide simple nonparametric methods to estimate finitemixture models fr...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
This paper provides methods to estimate finite mixtures from data with repeated measurements non-par...
textabstractFinite mixture distributions are a weighted average of a ¯nite number of distributions. ...
This thesis studies two types of research problems under finite mixture models. The first type is mi...
The important role of finite mixture models in the statistical analysis of data is underscored by th...
The aim is to study the asymptotic behavior of estimators and tests for the components of identifiab...
Estimating the model evidence - or mariginal likelihood of the data - is a notoriously difficult tas...
In many applications, observations from some distribution of interest are contaminated with errors...
Finite mixtures of probability distributions may be successfully used in the modeling of probability...
A recorded signal frequently results from the mixture of many signals from several classifiable sour...
Mixture models occur in numerous settings including random and fixed effects models, clustering, dec...
Finite Mixture models are a state-of-the-art technique of segmentation. Next tosegmenting consumers ...
We propose a maximum likelihood framework for estimating finite mixtures of multivariate regression ...
Mixtures of distributions are present in many econometric models, such as models with unobserved het...